Real Time H.264 Video Error Concealment Based on Motion Vector Prediction via Kalman Filter

2013 ◽  
pp. 11-18
Author(s):  
Walid El-Shafai ◽  
Branislav Hrušovský
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Error Concealment ◽  
Motion Vector ◽  
Video Error Concealment ◽  
Motion Vector Prediction

scholarly journals Robust Real-Time Detection of Laparoscopic Instruments in Robot Surgery Using Convolutional Neural Networks with Motion Vector Prediction

2019 ◽  
Vol 9 (14) ◽  
pp. 2865 ◽  
Author(s):  
Kyungmin Jo ◽  
Yuna Choi ◽  
Jaesoon Choi ◽  
Jong Woo Chung
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Convolutional Neural Networks ◽  
Detection System ◽  
Motion Vector ◽  
Detection Algorithm ◽  
Surgical Instruments ◽  
The Mean ◽  
Real Time Detection ◽  
Motion Vector Prediction

More than half of post-operative complications can be prevented, and operation performances can be improved based on the feedback gathered from operations or notifications of the risks during operations in real time. However, existing surgical analysis methods are limited, because they involve time-consuming processes and subjective opinions. Therefore, the detection of surgical instruments is necessary for (a) conducting objective analyses, or (b) providing risk notifications associated with a surgical procedure in real time. We propose a new real-time detection algorithm for detection of surgical instruments using convolutional neural networks (CNNs). This algorithm is based on an object detection system YOLO9000 and ensures continuity of detection of the surgical tools in successive imaging frames based on motion vector prediction. This method exhibits a constant performance irrespective of a surgical instrument class, while the mean average precision (mAP) of all the tools is 84.7, with a speed of 38 frames per second (FPS).

An Efficient Motion Vector Recovery and Reconstruction Method for Spatiotemporal Video Error Concealment

2019 ◽  
Vol 9 (4) ◽  
pp. 28-48
Author(s):  
Ansari Vaqar Ahmed ◽  
Uday Pandit Khot
Keyword(s):  
Mahalanobis Distance ◽  
Error Concealment ◽  
Motion Vector ◽  
Reconstruction Method ◽  
Recovery Method ◽  
Video Error Concealment ◽  
Distance Approach ◽  
Measurement Approach ◽  
Measure Technique ◽  
Total Average

In this article, an efficient spatiotemporal video error concealment (EC) based on motion vector (MV) recovery and a pixel reconstruction (PR) method is proposed. The pixel-based motion vector with partition (PMVP) is modified by using Mahalanobis distance (MD) rather than Euclidean distance (ED) for recovering MVs, as MD uses standard deviation and covariance of available pixels. Further, the MD gives more accuracy for non-square cluster compared to ED. This modified pixel-based motion vector with partition (MPMVP) algorithm is further upgrade by two different strategies. First, by using voting priority of available MVs based on the probabilities of similar directions. Second, by considering separate horizontal and vertical directions of available MVs in voting priority. For pixel reconstruction, modified spiral pixel reconstruction (MSPR) algorithm based on directional edge recovery method using minimum and maximum Mahalanobis distance from available pixels of surrounding MBs is proposed. Mahalanobis distance approach is most optimized similarity measure technique compared to other distance measurement approach to obtained lost motion vectors. These proposed EC techniques are compared with existing EC techniques like, SPR EC using ED, PMVP based EC with ED, and MV Interpolation by Zhou's method for various packet loss rates (PLRs) as 3%, 7%, 16%, 20% and quantization parameters (QPs) as 20, 24, 28, 32, 36. For total average in PLR of 3%, 7%, 16% and 20%, MSPR is having better PSNR compared to PMVP by 2.516, 2.29, 2.06 and 2.02 dB, respectively; and compared to SPR by 0.796, 0.718, 0.643 and 0.631 dB, respectively.

Motion Consistence and Textural Coherence based Error Concealment Algorithm for Corrupted Macroblock

2011 ◽  
Vol 383-390 ◽  
pp. 1605-1610
Author(s):  
Jing Chen ◽  
Can Hui Cai
Keyword(s):  
Reference Frame ◽  
Video Transmission ◽  
Error Concealment ◽  
State Of The Art ◽  
Motion Vector ◽  
Visual Quality ◽  
The State ◽  
Boundary Matching ◽  
Video Error Concealment

In this paper, an error concealment algorithm for lost macroblock (MB), named motion consistence and textural coherence based error concealment algorithm (MCTC), is proposed to meet the requirement of video transmission over error-prone channels. A directional predicted motion vector (MV) set is setup by using the motion consistence between MV co-located in reference frame and the neighboring MVs of the lost MB. To find out an optimal MV from this candidate MV set, a textural coherence based boundary matching (TCBM) criterion is proposed. The experiment results show that the MCTC outperforms the state-of-the-art video error concealment methods in both objective and subjective visual quality.

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